An RL agent typically does not have knowledge of the transition function \(\mathbb {P}\) .
Instead, it obtains a (finite) reward \(r\)  for each action that it takes.
Through repeated interactions with the environment, the agent seeks to learn a policy \(\pi \)  in order to maximize an objective \(\mathbb {E}_\pi [\sum _t \gamma ^t r(s_t,a_t)]\)  [1]}.
A policy is a probability distribution over the set of actions at a given state, and is denoted \(\pi (\cdot |s)\) .
In realistic scenarios, the expected reward might not be an adequate representation of decision-making.
This will necessitate the incorporation of risk-sensitivity into the RL framework.
